Types of Matrix || Linear Algebra for Machine Learning

Types of Matrix

overview :
Discover how different types of matrices drive machine learning! Learn about the role of square, symmetric, triangular, diagonal, identity, and orthogonal matrices in data transformation, optimization, and more. Perfect for ML enthusiasts looking to deepen their understanding of these fundamental concepts. 📊

Overview :

Matrices are fundamental to machine learning, and understanding their types and uses can significantly enhance your grasp of ML algorithms. In this video, we delve into the crucial roles of different matrices:

Types of Matirx
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Matrix itself is a large topic in Linear Algebra it has different use case and types . In this video I share some common type of Matrix that often see on when we perform machine learning stuff . We can say that this is the heart of #artificialintelligence

Types of Matirx
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1. Square Matrix
2. Symmetric Matrix
3. Triangular Matrix
4. Diagonal Matrix
5. Identity Matrix
6. Orthogonal Matrix

Use case of the matrix on ML
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Square Matrices: Key in linear transformations and algorithms like Principal Component Analysis (PCA) and linear regression.
Symmetric Matrices: Simplify computations in covariance matrices and are essential in algorithms like Singular Value Decomposition (SVD).

Triangular Matrices: Used in LU decomposition for solving linear systems, inverting matrices, and computing determinants.

Diagonal Matrices: Important for scaling, normalization, and dimensionality reduction techniques, including PCA and regularization methods.

Note : this video is special for those who have no idea about Matrix or those who want to know how matrix contribute on the Machine Learning . Thanks

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